Multi-objective Bayesian optimization (MOBO) has been the subject of some recent studies. BoTorch [1]}, the popular BO framework, uses the EHVI and ParEGO based on the works of [2]} and [3]}, [4]}. Hyper-volume improvement is the main mechanism used to ensure diversity in generations. `\(Q\) -batch' parallel settings of the above two acquisition functions use hyper-volume improvement and the previously selected point in the same batch to choose the next set of points. For most single-objective BO algorithms with parallel batch selection, the next batch of test points is selected by adding the `pretend' cost-function evaluation to the previously selected test point within that batch. However, this commonly used method often leads to overly confident test point selection, and the surrogate surface then needs to be optimized, and often refitted \(Q\)  times. Using GA, we can select a `\(Q\) -batch' of points with a single optimization of the surrogate surface from the GA generation.
